src/docIQ_overview.md

DocIQ: Knowledge Engine

Secure, cloud/On-Premise Generative AI utilizing Open Source RAG Architecture

Architecture: CPU-Optimized RAG

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Overview

DocIQ represents a paradigm shift in enterprise document intelligence. By leveraging a high-efficiency Retrieval-Augmented Generation (RAG) architecture, DocIQ delivers accurate, context-aware answers from internal knowledge bases without data ever leaving the application boundary.

Unlike cloud-dependent solutions, DocIQ operates entirely on open-source technology, optimized for constrained environments (2 vCPU), demonstrating that enterprise-grade AI does not require massive GPU clusters—only smart architecture.

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Technical Architecture

The system utilizes a decoupled "retriever-generator" architecture designed for maximum throughput on minimal hardware.

Component	Technology / Model	Enterprise Function
Generative LLM	MBZUAI/LaMini-Flan-T5-248M	Inference Engine: A distilled 248M parameter model fine-tuned for instruction following, capable of running efficiently on CPUs while minimizing hallucinations.
Embedding Engine	sentence-transformers/all-MiniLM-L6-v2	Semantic Indexing: Converts text to vector embeddings locally with high semantic density and millisecond latency.
Vector Database	ChromaDB (Persistent)	Knowledge Store: Serverless, persistent vector storage allowing instant semantic search across thousands of document chunks.
Orchestration	Python 3.10 + Streamlit	Frontend/Backend: Unified interface for ingestion, querying, and administrative management.
Caching Layer	SQLite + Fuzzy Logic	Performance: Local caching database to store QA history and serve repeated questions instantly.

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Key Features

1. Role-Based Access Control (RBAC)

Security is foundational. DocIQ implements session-based authentication to segregate duties, complying with standard data governance policies.

• Admin Role: Full privileges for document ingestion, topic creation, sub-topic management, and database cleaning.
• Viewer Role: Restricted "Read-Only" access. Viewers can query the knowledge base and provide feedback but cannot alter the vector index or delete audit logs.

2. Intelligent Adaptive Chunking

To maximize the context window of efficient LLMs, DocIQ employs sophisticated segmentation strategies during ingestion:

• Adaptive NLTK Chunking: Utilizes Natural Language Processing to respect sentence boundaries, ensuring semantic context is never severed mid-sentence.
• Recursive & Sliding Window: Fallback mechanisms ensure robust handling of unstructured text and OCR data.
• Metadata Tagging: Every chunk is tagged with topic, sub_topic, and chunking_strategy for precise retrieval filtering.

3. Integrated Hallucination Metrics & QA

Trust is the barrier to AI adoption. DocIQ builds trust through transparent observability displayed with every answer:

• Cosine Similarity Score: Mathematically verifies how closely the generated answer aligns with the source documents.
• ROUGE Metrics (2 & L): Measures the n-gram overlap to ensure the model isn't inventing new facts.
• Source Citation: Every answer includes a JSON log of the exact source_docs used to generate the response.

4. Smart Caching & Feedback Loops

The system accelerates over time using a local Metadata Layer.

• Fuzzy Match Caching: If a user asks a question similar to a previous one (e.g., >78% similarity), the system serves a pre-validated answer instantly, bypassing the inference engine.
• RLHF-Lite (Feedback): Users can vote "Helpful" 👍 or "Not Helpful" 👎. This data is stored in SQLite to build a dataset for future model fine-tuning.

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Operational Capabilities

Ingestion Module

"Turn static PDFs into dynamic knowledge."

• Auto-detection of topics and sub-topics from filenames.
• Extraction of tabular data utilizing Camelot.
• OCR Fallback: Automatically utilizes Tesseract and pdf2image if no text layer is detected in scanned PDFs.

Admin Console & Audit

"Full Observability."

• Strategy Overview: Visual analytics showing which chunking strategies are applied across the document estate.
• Topic Management: Granular deletion capabilities (delete entire topics or specific sub-topics).
• Audit Logging: Automatic generation of deletion reports confirming when and what data was purged.

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Infrastructure & Cost Efficiency

This application is engineered to run on :

• Compute: 2 vCPU / 16GB RAM.
• GPU Requirement: None.
• Data Sovereignty: All processing is local. No data is transmitted to OpenAI, Anthropic, or Azure.
• API tokens Cost: $0.00.

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Conclusion

DocIQ validates that secure, private, and intelligent Knowledge Management can be achieved using open-source tools. by intelligently combining ChromaDB for storage, Sentence-Transformers for understanding, and LaMini for generation, it provides an Enterprise-Grade RAG solution compliant with strict data privacy requirements.